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Purpose To predict potential treatment need during treat-and-extend (T&E) anti-vascular endothelial growth factor (VEGF) treatment in neovascular age-related macular degeneration (nAMD) using an artificial intelligence (AI) model trained using transfer learning. Methods ARIES and ALTAIR were randomized controlled Phase 3b/4 trials assessing intravitreal aflibercept (IVT-AFL) in patients with nAMD. Following treatment initiation with three monthly injections of IVT-AFL, treatment intervals were re-assessed continuously during the study based on prespecified criteria. In this post- hoc analysis, spectral domain optical coherence tomography (SD-OCT) scans from Week (Wk) 8 and Wk 16 visits from patients treated with T&E regimens of 2 mg IVT-AFL over 2 years were utilized to predict individual treatment intervals and frequency. Automated image segmentation of the SD-OCT scans was performed, predictive models of treatment intervals and frequency were developed using machine learning or logistic regression methods, and their performance was evaluated using a fivefold cross-validation. A transfer learning technique was used to adapt existing AI models previously trained on a pro-re-nata therapy regimen to the T&E dataset. Results In total, 205 ARIES and 112 ALTAIR patient datasets were used for training and evaluation. The following results were achieved with an AI model trained using transfer learning (for ARIES) and logistic regression (for ALTAIR). For prediction of the first treatment interval (short [< 12 weeks] or long [≥ 12 weeks]) following treatment initiation, at Visit 4 (Wk 16), the AI model achieved an area under the receiver operating characteristic curve (AUC) of 0.87 and 0.78 for ARIES and ALTAIR, respectively. For assessment of the individual frequency of IVT-AFL in the first and second study years, the model achieved an AUC of 0.84 and 0.79, respectively, for ARIES, and 0.79 and 0.78, respectively, for ALTAIR. For prediction of the last intended individual treatment interval at the end of Year 2, the AI model achieved an AUC of 0.74 and 0.77 for ARIES and ALTAIR, respectively. Conclusion AI trained using transfer learning can be used to predict potential treatment needs for anti-VEGF treatment in nAMD based on SD-OCT scans at Wk 8 and Wk 16, supporting medical decisions on interval adjustments and optimizing individualized IVT-AFL treatment regimens. Key messages What is known Due to the high treatment burden associated with frequent clinic visits and anti-vascular endothelial growth factor injections required for the treatment of patients with neovascular age-related macular degeneration (nAMD), it would be beneficial to be able to predict potential future treatment need, at initial stages of treatment. What is new An artificial intelligence (AI) model, previously trained with a real-world pro re nata -treated cohort, can be re-trained and applied to datasets from patients treated with treat-and extend regimens in the clinical study setting to predict potential treatment need. AI may be used to predict potential treatment needs for intravitreal aflibercept treatment in nAMD based on optical coherence tomography scans at Weeks 8 and 16, supporting medical decisions on interval adjustments and optimizing individualized treatment regimens.

A considerable body of evidence derived from terror management theory indicates that the awareness of mortality represents a potent psychological threat engendering various forms of psychological defense. However, extant research has yet to examine the neurological correlates of cognitions about one’s inevitable death. The present study thus investigated in 17 male participants patterns of neural activation elicited by mortality threat. To induce mortality threat, participants answered questions arranged in trial blocks that referred to fear of death and dying. In the control condition participants answered questions about fear of dental pain. Neural responses to mortality threat were greater than to pain threat in right amygdala, left rostral anterior cingulate cortex, and right caudate nucleus. We discuss implications of these findings for stimulating further research into the neurological correlates of managing existential fear.

We suggest to utilize the rich information content about microstructural tissue properties entangled in asymmetric balanced steady-state free precession (bSSFP) profiles to estimate multiple diffusion metrics simultaneously by neural network (NN) parameter quantification. A 12-point bSSFP phase-cycling scheme with high-resolution whole-brain coverage is employed at 3 T and 9.4 T for NN input. Low-resolution target diffusion data are derived based on diffusion-weighted spin-echo echo-planar-imaging (SE-EPI) scans, i.e., mean, axial, and radial diffusivity (MD, AD, RD), fractional anisotropy (FA) as well as the spherical coordinates (azimuth \\(\\) and inclination \\(\\)) of the principal diffusion eigenvector. A feedforward NN is trained with incorporated probabilistic uncertainty estimation. The NN predictions yielded highly reliable results in white matter (WM) and gray matter (GM) structures for MD. The quantification of FA, AD, and RD was overall in good agreement with the reference but the dependence of these parameters on WM anisotropy was somewhat biased, e.g., in corpus callosum. The inclination \\(\\) was well predicted for anisotropic WM structures while the azimuth \\(\\) was overall poorly predicted. The findings were highly consistent across both field strengths. Application of the optimized NN to high-resolution input data provided whole-brain maps with rich structural details. In conclusion, the proposed NN-driven approach showed potential to provide distortion-free high-resolution whole-brain maps of multiple diffusion metrics at high to ultra-high field strengths in clinically relevant scan times.

Purpose: A supervised learning framework is proposed to automatically generate MR sequences and corresponding reconstruction based on the target contrast of interest. Combined with a flexible, task-driven cost function this allows for an efficient exploration of novel MR sequence strategies. Methods: The scanning and reconstruction process is simulated end-to-end in terms of RF events, gradient moment events in x and y, and delay times, acting on the input model spin system given in terms of proton density, T1 and T2, and \\(\\)B0. As a proof of concept, we use both conventional MR images and T1 maps as targets and optimize from scratch using the loss defined by data fidelity, SAR penalty, and scan time. Results: In a first attempt, MRzero learns gradient and RF events from zero, and is able to generate a target image produced by a conventional gradient echo sequence. Using a neural network within the reconstruction module allows arbitrary targets to be learned successfully. Experiments could be translated to image acquisition at the real system (3T Siemens, PRISMA) and could be verified in the measurements of phantoms and a human brain in vivo. Conclusions: Automated MR sequence generation is possible based on differentiable Bloch equation simulations and a supervised learning approach.

Eigenvector centrality mapping (ECM) is a popular technique for analyzing fMRI data of the human brain. It is used to obtain maps of functional hubs in networks of the brain in a manner similar to Google’s PageRank algorithm. ECM attributes a score to the time course of each voxel that reflects its centrality within the network. Voxels that are strongly correlated with many other voxels that are themselves strongly correlated with other voxels receive high scores. Currently, there exist two different implementations ECM, one of which is very fast but limited to one particular type of correlation metric whose interpretation can be problematic. The second implementation supports many different metrics, but it is computationally costly and requires a very large main memory. Here we propose two new implementations of the ECM approach that resolve these issues. The first is based on a new correlation metric that we call “ReLU correlation (RLC)”. The second method is based on matrix projections. We demonstrate the use of both techniques on standard fMRI data, as well as on high-resolution fMRI data acquired at 9.4 Tesla.